

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__snake_case				:      Optional[int]   		=			logging.get_logger(__name__)

__snake_case				:      Dict   		=			{
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}



class        A__(a_							):
						"""simple docstring"""






						_A    :				List[str]       					=			'''instructblip_vision_model'''





						def __init__(    self  ,      _lowercase=1_408  ,      _lowercase=6_144  ,      _lowercase=39  ,      _lowercase=16  ,      _lowercase=224  ,      _lowercase=14  ,      _lowercase="gelu"  ,      _lowercase=1e-6  ,      _lowercase=0.0  ,      _lowercase=1e-10  ,      _lowercase=True  ,      **_lowercase  ,      )     -> Dict:
									super().__init__(**_lowercase    )

									a_						:						Tuple 							= hidden_size
									a_						:						int 							= intermediate_size
									a_						:						Dict 							= num_hidden_layers
									a_						:						Optional[Any] 							= num_attention_heads
									a_						:						List[Any] 							= patch_size
									a_						:						List[str] 							= image_size
									a_						:						int 							= initializer_range
									a_						:						Optional[int] 							= attention_dropout
									a_						:						str 							= layer_norm_eps
									a_						:						Dict 							= hidden_act
									a_						:						Optional[int] 							= qkv_bias





						@classmethod
						def 			UpperCamelCase__					(    cls  ,      _lowercase  ,      **_lowercase    )     -> "PretrainedConfig":
									cls._set_token_in_kwargs(_lowercase    )

									a_							,					a_						:						int 							= cls.get_config_dict(_lowercase  ,      **_lowercase    )

									# get the vision config dict if we are loading from InstructBlipConfig
									if config_dict.get("""model_type"""    ) == "instructblip":
												a_						:						Union[str, Any] 							= config_dict["""vision_config"""]

									if "model_type" in config_dict and hasattr(cls  ,      """model_type"""    ) and config_dict["model_type"] != cls.model_type:
												logger.warning(
												    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
												    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''    )

									return cls.from_dict(_lowercase  ,      **_lowercase    )







class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[Any]       					=			'''instructblip_qformer'''





						def __init__(    self  ,      _lowercase=30_522  ,      _lowercase=768  ,      _lowercase=12  ,      _lowercase=12  ,      _lowercase=3_072  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=0.0_2  ,      _lowercase=1e-12  ,      _lowercase=0  ,      _lowercase="absolute"  ,      _lowercase=2  ,      _lowercase=1_408  ,      **_lowercase  ,      )     -> Optional[int]:
									super().__init__(pad_token_id=_lowercase  ,      **_lowercase    )

									a_						:						List[Any] 							= vocab_size
									a_						:						Union[str, Any] 							= hidden_size
									a_						:						Optional[Any] 							= num_hidden_layers
									a_						:						List[Any] 							= num_attention_heads
									a_						:						Optional[Any] 							= hidden_act
									a_						:						Optional[Any] 							= intermediate_size
									a_						:						Tuple 							= hidden_dropout_prob
									a_						:						List[Any] 							= attention_probs_dropout_prob
									a_						:						Any 							= max_position_embeddings
									a_						:						str 							= initializer_range
									a_						:						int 							= layer_norm_eps
									a_						:						Optional[int] 							= position_embedding_type
									a_						:						List[Any] 							= cross_attention_frequency
									a_						:						Union[str, Any] 							= encoder_hidden_size





						@classmethod
						def 			UpperCamelCase__					(    cls  ,      _lowercase  ,      **_lowercase    )     -> "PretrainedConfig":
									cls._set_token_in_kwargs(_lowercase    )

									a_							,					a_						:						str 							= cls.get_config_dict(_lowercase  ,      **_lowercase    )

									# get the qformer config dict if we are loading from InstructBlipConfig
									if config_dict.get("""model_type"""    ) == "instructblip":
												a_						:						Union[str, Any] 							= config_dict["""qformer_config"""]

									if "model_type" in config_dict and hasattr(cls  ,      """model_type"""    ) and config_dict["model_type"] != cls.model_type:
												logger.warning(
												    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
												    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''    )

									return cls.from_dict(_lowercase  ,      **_lowercase    )







class        A__(a_							):
						"""simple docstring"""






						_A    :				Tuple       					=			'''instructblip'''
						_A    :				Dict       					=			True





						def __init__(    self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=32  ,      **_lowercase    )     -> List[str]:
									super().__init__(**_lowercase    )

									if vision_config is None:
												a_						:						Union[str, Any] 							= {}
												logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""    )

									if qformer_config is None:
												a_						:						List[str] 							= {}
												logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."""    )

									if text_config is None:
												a_						:						List[Any] 							= {}
												logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""    )

									a_						:						Any 							= InstructBlipVisionConfig(**_lowercase    )
									a_						:						str 							= InstructBlipQFormerConfig(**_lowercase    )
									a_						:						str 							= text_config["""model_type"""] if """model_type""" in text_config else """opt"""
									a_						:						Dict 							= CONFIG_MAPPING[text_model_type](**_lowercase    )

									a_						:						Dict 							= self.text_config.tie_word_embeddings
									a_						:						int 							= self.text_config.is_encoder_decoder

									a_						:						Optional[Any] 							= num_query_tokens
									a_						:						Optional[Any] 							= self.vision_config.hidden_size
									a_						:						Tuple 							= self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
									a_						:						Tuple 							= 1.0
									a_						:						Dict 							= 0.0_2





						@classmethod
						def 			UpperCamelCase__					(    cls  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase  ,      )     -> List[Any]:
									return cls(
									    vision_config=vision_config.to_dict()  ,      qformer_config=qformer_config.to_dict()  ,      text_config=text_config.to_dict()  ,      **_lowercase  ,      )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						Dict 							= copy.deepcopy(self.__dict__    )
									a_						:						List[str] 							= self.vision_config.to_dict()
									a_						:						Optional[int] 							= self.qformer_config.to_dict()
									a_						:						int 							= self.text_config.to_dict()
									a_						:						Optional[Any] 							= self.__class__.model_type
									return output





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:      Any   		=			logging.get_logger(__name__)

__snake_case				:      Union[str, Any]   		=			{
    """microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class        A__(a_							):
						"""simple docstring"""






						_A    :				Union[str, Any]       					=			'''cvt'''





						def __init__(    self  ,      _lowercase=3  ,      _lowercase=[7, 3, 3]  ,      _lowercase=[4, 2, 2]  ,      _lowercase=[2, 1, 1]  ,      _lowercase=[64, 192, 384]  ,      _lowercase=[1, 3, 6]  ,      _lowercase=[1, 2, 10]  ,      _lowercase=[4.0, 4.0, 4.0]  ,      _lowercase=[0.0, 0.0, 0.0]  ,      _lowercase=[0.0, 0.0, 0.0]  ,      _lowercase=[0.0, 0.0, 0.1]  ,      _lowercase=[True, True, True]  ,      _lowercase=[False, False, True]  ,      _lowercase=["dw_bn", "dw_bn", "dw_bn"]  ,      _lowercase=[3, 3, 3]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=[2, 2, 2]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=0.0_2  ,      _lowercase=1e-12  ,      **_lowercase  ,      )     -> Tuple:
									super().__init__(**_lowercase    )
									a_						:						str 							= num_channels
									a_						:						Optional[int] 							= patch_sizes
									a_						:						Dict 							= patch_stride
									a_						:						int 							= patch_padding
									a_						:						int 							= embed_dim
									a_						:						Tuple 							= num_heads
									a_						:						Optional[Any] 							= depth
									a_						:						str 							= mlp_ratio
									a_						:						Optional[int] 							= attention_drop_rate
									a_						:						int 							= drop_rate
									a_						:						List[Any] 							= drop_path_rate
									a_						:						List[Any] 							= qkv_bias
									a_						:						Dict 							= cls_token
									a_						:						Union[str, Any] 							= qkv_projection_method
									a_						:						List[Any] 							= kernel_qkv
									a_						:						Optional[Any] 							= padding_kv
									a_						:						Any 							= stride_kv
									a_						:						Dict 							= padding_q
									a_						:						Optional[int] 							= stride_q
									a_						:						Tuple 							= initializer_range
									a_						:						int 							= layer_norm_eps


